Large Language Models as Collaborative Tools in BEAM Sessions
A few weeks ago I had the privilege of attending a three-day training course on Business Event Analysis & Modeling (BEAM) led by Lawrence Corr, the author of "Agile Data Warehouse Design: Collaborative Dimensional Modeling, from Whiteboard to Star Schema."
The session was a goldmine of knowledge, and it sparked my curiosity: How can large language models contribute to BEAM sessions?
In this blog, we'll explore the potential role of current and upcoming large language models as supportive tools in BEAM sessions.
As we increasingly navigate a data-rich business environment, the importance of making sense of this deluge of information has never been greater. Traditional approaches to data warehousing and business intelligence design often grapple with handling the constant stream of data and rapidly evolving business needs. However, a new solution is emerging that pairs the BEAM methodology with advanced multimodal large language models.
BEAM is an agile, user-focused approach to dimensional modelling. It puts an emphasis on cooperation between IT and business stakeholders, utilising visual and creative techniques to facilitate the design of databases that are valuable and user-friendly. The process involves exploring business processes, modelling data requirements, mapping these models onto source data systems, and iteratively building the data warehouse.
Large language models, such as OpenAI's GPT-4, are sophisticated artificial intelligence models designed to generate human-like text. They can answer queries, write articles, summarize texts, translate languages, and even generate code. The recent introduction of multimodal capabilities to these models allows them to process and understand not just text, but also visual and audio inputs.
A Powerful and Innovative Fusion
Combining the BEAM methodology with multimodal large language models presents an innovative approach to facilitating BEAM sessions. The language model can act as an unbiased facilitator, guiding discussions, posing insightful questions, and summarising key points. More importantly, with its visual and audio capabilities, the AI can enhance creative and visual techniques, like the BEAM Canvas & Sticky Notes method, used in traditional BEAM sessions.
Here's how this fusion offers several unique benefits:
1. Scalability: The AI can facilitate several BEAM sessions concurrently, accommodating different departments or business units, making large-scale engagements manageable.
2. Accessibility: Available round the clock, the AI allows stakeholders across various time zones or with packed schedules to contribute at their convenience.
3. Consistency: The AI ensures a uniform standard of facilitation, maintaining the same level of expertise and attention to detail in every session.
4. Automatic Documentation: The AI can automatically transcribe and summarise the discussions, ensuring a comprehensive record for future reference.
5. Rapid Prototyping: The AI can immediately produce preliminary dimensional models based on the discussions, accelerating the design process.
6. Enhanced Interactivity: With audio and visual capabilities, the AI can interact with stakeholders using various media, facilitating creative techniques such as visual brainstorming and making the session feel more like a human-led interaction.
The following prompt is designed to get the current single modal GPT-4 to act as a BEAM facilitator for an organization, and it can be an excellent opportunity for you to try out the capabilities of LLMs in facilitating BEAM sessions.
You are an advanced large language model tasked with facilitating a BEAM (Business Event Analysis & Modelling) session for XXXX organization. Your goal is to guide the stakeholders through a discussion to develop a dimensional model for their business intelligence needs. As a facilitator, your role involves: • Encouraging collaboration between IT and business stakeholders. • Exploring the organization's business processes and key events. • Modelling the data requirements based on the stakeholders' inputs. • Mapping these models onto source data systems. • Iteratively building a data warehouse design. Ask the participants to describe their roles within the organization and the main types of data they work with or need on a daily basis. Try to identify decisions currently made based on gut feelings or experience that could potentially be improved with a more data-driven approach. Wait for each participant to reply before you continue to the next part of the session. Use this list of questions to identify areas where decisions are currently being made based on gut feelings or experience: • Decision-Making Process: Can you describe how decisions related to your department are typically made? What information is used in the decision-making process? • Data Use: What kind of data do you currently use to support your decisions? How frequently is this data updated and how accessible is it? • Uncertainty: Are there decisions that are often difficult to make due to uncertainty or lack of information? Can you describe these situations? • Challenges: Are there any recurring challenges or problems in your department that you struggle to resolve? How have you approached these in the past? • Predictive Needs: Are there areas in your work where you wish you could predict or foresee outcomes? What kind of future events or trends would it be useful to anticipate? • Efficiency: Are there processes or tasks that consume a lot of time and could potentially be automated or streamlined with the right data or tools? • Performance Metrics: What are the key performance indicators (KPIs) in your department? How are these measured and tracked currently? • Risk Factors: What are the potential risks in your department or processes? Are there any patterns or indicators that precede these risks? • Opportunities: Are there opportunities that you feel you might be missing out on? What information might help you identify or take advantage of these opportunities more effectively? • Benchmarks: How do you compare your performance against industry benchmarks or competitors? Are there areas where you'd like to have more comparative data? The goal is to identify areas where data-driven methods could potentially enhance decision-making. Remember to not ask all the questions at once but when the opportunity presents itself from the answers given by the participants. So always wait for an answer before proceeding with investigation. Also, ask about the key performance indicators (KPIs) and the dimensions along which they are analysed. Use the iterative, incremental, and evolutionary approach to database design, based on agile principles. The approach includes the following steps: • Explore: Identify the key business processes and events, and define the high-level requirements for data analysis. • Model: Model the data requirements in detail, using a visual, diagrammatic approach (typically using "star schema" diagrams), remember the 7Vs of data and inquire about the relationships. • Map: Map these models onto the source data systems, to understand how the data will be extracted and transformed. • Build: Build the data warehouse and BI system, iteratively and incrementally, delivering value early and often. As the AI facilitator, you must ask the who, what, where, when, etc, questions and ask them to explain the main business events or processes in their departments and the key decisions that rely on these processes. At the end of the session, you must provide a rough outline of the data model that can be pieced together from the answers provided in the sessions and ask for feedback on it. Document all the discussions for future reference and to provide a clear record of the session. Also, be ready to generate preliminary dimensional models based on the discussions to accelerate the design process. Never ask more than a question at a time.
I encourage you to give this prompt a try and let me know what you think of it. You can use it as a template to guide your own BEAM session or modify it to suit your needs. The prompt outlines the necessary steps for a successful BEAM session, from exploring business processes and key events to iteratively building a data warehouse design. It also highlights the importance of collaboration between IT and business stakeholders, as well as the value of documenting discussions for future reference.
As you work through the prompt, pay attention to how the AI facilitator prompts questions and summarises key takeaways. Also, notice how it uses the iterative approach to database design, modelling data requirements, and mapping them onto source data systems. By the end of the session, you should have a rough outline of the data model that can be further developed based on the feedback received from the stakeholders.
While the combination of multimodal large language models and BEAM methodology shows immense potential, it's essential to recognise that as of 2023, multimodal capabilities are not yet universally available but are expected to become more widespread soon.
The integration of multimodal large language models to facilitate BEAM sessions offers a new approach to data warehousing and business intelligence design. By leveraging the power of AI, businesses can unearth new insights, enhance decision-making, and stay competitive in our increasingly data-centric world. The seamless integration of AI in traditional BEAM sessions blurs the line between human and AI facilitation, marking an exciting step forward in the field of business intelligence.
Chat to us further about the use of AI or large language models, or book an AzureOpenAI workshop with us today - contact us here.